
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

__lowerCAmelCase     :	int       			=  imread(R'digital_image_processing/image_data/lena_small.jpg')
__lowerCAmelCase     :	Any       			=  cvtColor(img, COLOR_BGR2GRAY)

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			cn.convert_to_negative(A_   )
  # assert negative_img array for at least one True
  assert negative_img.any()

def a__       (			):






  '''simple docstring'''





  with Image.open("""digital_image_processing/image_data/lena_small.jpg"""   ) as img:
    # Work around assertion for response
    assert str(cc.change_contrast(A_,	110   )   ).startswith(
        """<PIL.Image.Image image mode=RGB size=100x100 at"""   )

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			canny.gen_gaussian_kernel(9,	sigma=1.4   )
  # Assert ambiguous array
  assert resp.all()

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			imread("""digital_image_processing/image_data/lena_small.jpg""",	0   )
  # assert ambiguous array for all == True
  assert canny_img.all()
  __magic_name__								=			canny.canny(A_   )
  # assert canny array for at least one True
  assert canny_array.any()

def a__       (			):






  '''simple docstring'''





  assert gg.gaussian_filter(A_,	5,	sigma=0.9   ).all()

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]   )
  __magic_name__								=			conv.img_convolve(A_,	A_   ).astype(A_   )
  assert res.any()

def a__       (			):






  '''simple docstring'''





  assert med.median_filter(A_,	3   ).any()

def a__       (			):






  '''simple docstring'''





  __magic_name__	,       __magic_name__								=			sob.sobel_filter(A_   )
  assert grad.any() and theta.any()

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			sp.make_sepia(A_,	20   )
  assert sepia.all()

def a__       (			A_ = "digital_image_processing/image_data/lena_small.jpg"   ):






  '''simple docstring'''





  __magic_name__								=			bs.Burkes(imread(A_,	1   ),	120   )
  burkes.process()
  assert burkes.output_img.any()

def a__       (			A_ = "digital_image_processing/image_data/lena_small.jpg",	):






  '''simple docstring'''





  __magic_name__								=			rs.NearestNeighbour(imread(A_,	1   ),	400,	200   )
  nn.process()
  assert nn.output.any()





def a__       (			):






  '''simple docstring'''





  __magic_name__								=			"""digital_image_processing/image_data/lena.jpg"""

  # Reading the image and converting it to grayscale.
  __magic_name__								=			imread(A_,	0   )

  # Test for get_neighbors_pixel function() return not None
  __magic_name__								=			0
  __magic_name__								=			0
  __magic_name__								=			image[x_coordinate][y_coordinate]

  __magic_name__								=			lbp.get_neighbors_pixel(
      A_,	A_,	A_,	A_   )

  assert neighbors_pixels is not None

  # Test for local_binary_pattern function()
  # Create a numpy array as the same height and width of read image
  __magic_name__								=			np.zeros((image.shape[0], image.shape[1])   )

  # Iterating through the image and calculating the local binary pattern value
  # for each pixel.
  for i in range(0,	image.shape[0]   ):
    for j in range(0,	image.shape[1]   ):
      __magic_name__								=			lbp.local_binary_value(A_,	A_,	A_   )

  assert lbp_image.any()



import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
    import bsa
    from bsa import BeautifulSoup


__lowerCAmelCase     :	int       			=  logging.get_logger(__name__)




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  def __init__(					self     :					Union[str, Any]     ,				**UpperCamelCase__     :					Union[str, Any]       )     ->      Union[str, Any]:







    """simple docstring"""






    requires_backends(self     ,				["""bs4"""]       )
    super().__init__(**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]
    __magic_name__								=			element if element.name else element.parent
    for parent in child.parents:  # type: bs4.element.Tag
      __magic_name__								=			parent.find_all(child.name     ,				recursive=UpperCamelCase__       )
      xpath_tags.append(child.name       )
      xpath_subscripts.append(
          0 if 1 == len(UpperCamelCase__       ) else next(i for i, s in enumerate(UpperCamelCase__     ,				1       ) if s is child       )       )
      __magic_name__								=			parent
    xpath_tags.reverse()
    xpath_subscripts.reverse()
    return xpath_tags, xpath_subscripts
  def 				_lowercase				(					self     :					Union[str, Any]     ,				UpperCamelCase__     :					List[Any]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			BeautifulSoup(UpperCamelCase__     ,				"""html.parser"""       )

    __magic_name__								=			[]
    __magic_name__								=			[]
    __magic_name__								=			[]

    for element in html_code.descendants:
      if type(UpperCamelCase__       ) == bsa.element.NavigableString:
        if type(element.parent       ) != bsa.element.Tag:
          continue

        __magic_name__								=			html.unescape(UpperCamelCase__       ).strip()
        if not text_in_this_tag:
          continue

        all_doc_strings.append(UpperCamelCase__       )

        __magic_name__	,       __magic_name__								=			self.xpath_soup(UpperCamelCase__       )
        stringaxtag_seq.append(UpperCamelCase__       )
        stringaxsubs_seq.append(UpperCamelCase__       )

    if len(UpperCamelCase__       ) != len(UpperCamelCase__       ):
      raise ValueError("""Number of doc strings and xtags does not correspond"""       )
    if len(UpperCamelCase__       ) != len(UpperCamelCase__       ):
      raise ValueError("""Number of doc strings and xsubs does not correspond"""       )

    return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
  def 				_lowercase				(					self     :					List[Any]     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					List[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			""""""
    for tagname, subs in zip(UpperCamelCase__     ,				UpperCamelCase__       ):
      xpath += F'''/{tagname}'''
      if subs != 0:
        xpath += F'''[{subs}]'''
    return xpath





  def __call__(					self     :					str     ,				UpperCamelCase__     :					Tuple       )     ->      BatchFeature:







    """simple docstring"""






    __magic_name__								=			False

    # Check that strings has a valid type
    if isinstance(UpperCamelCase__     ,				UpperCamelCase__       ):
      __magic_name__								=			True
    elif isinstance(UpperCamelCase__     ,				(list, tuple)       ):
      if len(UpperCamelCase__       ) == 0 or isinstance(html_strings[0]     ,				UpperCamelCase__       ):
        __magic_name__								=			True

    if not valid_strings:
      raise ValueError(
          """HTML strings must of type `str`, `List[str]` (batch of examples), """
          F'''but is of type {type(UpperCamelCase__       )}.'''       )

    __magic_name__								=			bool(isinstance(UpperCamelCase__     ,				(list, tuple)       ) and (isinstance(html_strings[0]     ,				UpperCamelCase__       ))       )

    if not is_batched:
      __magic_name__								=			[html_strings]

    # Get nodes + xpaths
    __magic_name__								=			[]
    __magic_name__								=			[]
    for html_string in html_strings:
      __magic_name__	,       __magic_name__	,       __magic_name__								=			self.get_three_from_single(UpperCamelCase__       )
      nodes.append(UpperCamelCase__       )
      __magic_name__								=			[]
      for node, tag_list, sub_list in zip(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       ):
        __magic_name__								=			self.construct_xpath(UpperCamelCase__     ,				UpperCamelCase__       )
        xpath_strings.append(UpperCamelCase__       )
      xpaths.append(UpperCamelCase__       )

    # return as Dict
    __magic_name__								=			{"""nodes""": nodes, """xpaths""": xpaths}
    __magic_name__								=			BatchFeature(data=UpperCamelCase__     ,				tensor_type=UpperCamelCase__       )

    return encoded_inputs

